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https://hdl.handle.net/1822/66310
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Campo DC | Valor | Idioma |
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dc.contributor.author | Ferreirinha, Luís | por |
dc.contributor.author | Santos, André S. | por |
dc.contributor.author | Madureira, Ana M. | por |
dc.contributor.author | Varela, M.L.R. | por |
dc.contributor.author | Bastos, João A. | por |
dc.date.accessioned | 2020-08-05T14:58:04Z | - |
dc.date.available | 2022-01-01T07:01:44Z | - |
dc.date.issued | 2020 | - |
dc.identifier.isbn | 9783030143466 | por |
dc.identifier.issn | 2194-5357 | - |
dc.identifier.uri | https://hdl.handle.net/1822/66310 | - |
dc.description.abstract | Production scheduling in the presence of real-time events is of great importance for the successful implementation of real-world scheduling systems. Most manufacturing systems operate in dynamic environments vulnerable to various stochastic real-time events which continuously forces reconsideration and revision of pre-established schedules. In an uncertain environment, efficient ways to adapt current solutions to unexpected events, are preferable to solutions that soon become obsolete. This reality motivated us to develop a tool that attempts to start filling the gap between scheduling theory and practice. The developed prototype is connected to the MRP software and uses meta heuristics to generate a predictive schedule. Then, whenever disruptions happen, like arrival of new tasks or cancelation of others, the tool starts rescheduling through a dynamic-event module that combines dispatching rules that best fit the performance measures pre-classified by Kano’s model. The proposed tool was tested in an in-depth computational study with dynamic task releases and stochastic execution time. The results demonstrate the effectiveness of the model. | por |
dc.description.sponsorship | - (undefined) | por |
dc.language.iso | eng | por |
dc.publisher | Springer Verlag | por |
dc.rights | openAccess | por |
dc.subject | Decision support tool | por |
dc.subject | Dispatching rules | por |
dc.subject | Dynamic scheduling | por |
dc.subject | Hyper heuristics | por |
dc.subject | Kano’s model | por |
dc.subject | Meta heuristics | por |
dc.title | Decision support tool for dynamic scheduling | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
oaire.citationStartPage | 418 | por |
oaire.citationEndPage | 427 | por |
oaire.citationVolume | 923 | por |
dc.date.updated | 2020-08-04T17:52:21Z | - |
dc.identifier.doi | 10.1007/978-3-030-14347-3_41 | por |
sdum.export.identifier | 5797 | - |
sdum.journal | Advances in Intelligent Systems and Computing | por |
oaire.version | AM | por |
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Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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Decision Support Tool to Dynamic Scheduling.pdf | 621,02 kB | Adobe PDF | Ver/Abrir |